Abstract
Reliable cortical parcellation is a crucial step in human brain network analysis since incorrect definition of nodes may invalidate the inferences drawn from the network. Cortical parcellation is typically cast as an unsupervised clustering problem on functional magnetic resonance imaging (fMRI) data, which is particularly challenging given the pronounced noise in fMRI acquisitions. This challenge manifests itself in rather inconsistent parcellation maps generated by different methods. To address the need for robust methodologies to parcellate the brain, we propose a multimodal cortical parcellation framework based on fused diffusion MRI (dMRI) and fMRI data analysis. We argue that incorporating anatomical connectivity information into parcellation is beneficial in suppressing spurious correlations commonly observed in fMRI analyses. Our approach adaptively determines the weighting of anatomical and functional connectivity information in a data-driven manner, and incorporates a neighborhood-informed affnity matrix that was recently shown to provide robustness against noise. To validate, we compare parcellations obtained via normalized cuts on unimodal vs. multimodal data from the Human Connectome Project. Results demonstrate that our proposed method better delineates spatially contiguous parcels with higher test-retest reliability and improves inter-subject consistency.
Chapter PDF
Similar content being viewed by others
Keywords
References
Thirion, B., Varoquaux, G., Dohmatob, E., Poline, J.-B.: Which fmri clustering gives good brain parcellations? Frontiers in Neuroscience 8 (2014)
de Reus, M.A., Van den Heuvel, M.P.: The parcellation-based connectome: limitations and extensions. Neuroimage 80, 397–404 (2013)
Honey, C., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J.P., Meuli, R., Hagmann, P.: Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences 106(6), 2035–2040 (2009)
Wang, J., Yang, Y., Fan, L., Xu, J., Li, C., Liu, Y., Fox, P.T., Eickhoff, S.B., Yu, C., Jiang, T.: Convergent functional architecture of the superior parietal lobule unraveled with multimodal neuroimaging approaches. Human Brain Mapping 36(1), 238–257 (2015)
Zhang, D., Snyder, A.Z., Shimony, J.S., Fox, M.D., Raichle, M.E.: Noninvasive functional and structural connectivity mapping of the human thalamocortical system. Cerebral Cortex 20(5), 1187–1194 (2010)
Wang, C., Yoldemir, B., Abugharbieh, R.: Improved functional cortical parcellation using a neighborhood-information-embedded affinity matrix. In: IEEE International Symposium on Biomedical Imaging, pp. 1340–1343. IEEE Press (2015)
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K., Consortium, W.M.H., et al.: The wu-minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)
Craddock, R.C., James, G.A., Holtzheimer, P.E., Hu, X.P., Mayberg, H.S.: A whole brain fmri atlas generated via spatially constrained spectral clustering. Human Brain Mapping 33(8), 1914–1928 (2012)
Glasser, M.F., Sotiropoulos, S.N., Wilson, J.A., Coalson, T.S., Fischl, B., Andersson, J.L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J.R., et al.: The minimal preprocessing pipelines for the human connectome project. Neuroimage 80, 105–124 (2013)
Neher, P.F., Stieltjes, B., Reisert, M., Reicht, I., Meinzer, H.P., Fritzsche, K.H.: Mitk global tractography. In: SPIE Medical Imaging, International Society for Optics and Photonics, pp. 83144D–83144D (2012)
Johansen-Berg, H., Behrens, T., Robson, M., Drobnjak, I., Rushworth, M., Brady, J., Smith, S., Higham, D., Matthews, P.: Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex. Proceedings of the National Academy of Sciences of the United States of America 101(36), 13335–13340 (2004)
Skudlarski, P., Jagannathan, K., Calhoun, V.D., Hampson, M., Skudlarska, B.A., Pearlson, G.: Measuring brain connectivity: diffusion tensor imaging validates resting state temporal correlations. Neuroimage 43(3), 554–561 (2008)
Wang, J., Fan, L., Zhang, Y., Liu, Y., Jiang, D., Zhang, Y., Yu, C., Jiang, T.: Tractography-based parcellation of the human left inferior parietal lobule. Neuroimage 63(2), 641–652 (2012)
Montaser-Kouhsari, L., Landy, M.S., Heeger, D.J., Larsson, J.: Orientation-selective adaptation to illusory contours in human visual cortex. The Journal of Neuroscience 27(9), 2186–2195 (2007)
Munkres, J.: Algorithms for the assignment and transportation problems. Journal of the Society for Industrial & Applied Mathematics 5(1), 32–38 (1957)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, C., Yoldemir, B., Abugharbieh, R. (2015). Multimodal Cortical Parcellation Based on Anatomical and Functional Brain Connectivity. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9351. Springer, Cham. https://doi.org/10.1007/978-3-319-24574-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-24574-4_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24573-7
Online ISBN: 978-3-319-24574-4
eBook Packages: Computer ScienceComputer Science (R0)